AIMC Topic: Dose-Response Relationship, Drug

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Cytotoxicity of chitosan/streptokinase nanoparticles as a function of size: An artificial neural networks study.

Nanomedicine : nanotechnology, biology, and medicine Sep 25, 2015
Predicting the size and toxicity of chitosan/streptokinase nanoparticles at various values of processing parameters was the aim of this study. For the first time, a comprehensive model could be developed to determine the cytotoxicity of the nanoparti...
Nanocomposites Pattern Recognition, Automated Toxicity Tests Streptokinase Sensitivity and Specificity Dose-Response Relationship, Drug Cell Survival Chitosan Particle Size Structure-Activity Relationship Hydrogen-Ion Concentration Drug Evaluation, Preclinical Neural Networks, Computer Algorithms Reproducibility of Results
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Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods.

European journal of medicinal chemistry Jul 6, 2015
The HIV-1 protease has proven to be a crucial component of the HIV replication machinery and a reliable target for anti-HIV drug discovery. In this study, we applied an optimized hierarchical multistage virtual screening method targeting HIV-1 protea...
HIV Protease Inhibitors Molecular Structure Drug Evaluation, Preclinical HIV Protease Molecular Docking Simulation Support Vector Machine Dose-Response Relationship, Drug Structure-Activity Relationship
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Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.

Journal of applied toxicology : JAT Jun 5, 2015
Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse ...
Humans Machine Learning Animal Testing Alternatives Skin Algorithms Skin Tests Markov Chains Local Lymph Node Assay Databases, Factual Quantitative Structure-Activity Relationship Hydrocarbons, Brominated Risk Assessment Toxicity Tests Dose-Response Relationship, Drug
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Revisiting Warfarin Dosing Using Machine Learning Techniques.

Computational and mathematical methods in medicine Jun 4, 2015
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographi...
Pharmacogenetics Humans Anticoagulants Genotype Databases, Factual Machine Learning Dose-Response Relationship, Drug Warfarin Algorithms Female Regression Analysis Drug Administration Schedule Reproducibility of Results False Positive Reactions Male
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Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.

Journal of medical systems Dec 4, 2014
The spectrum of EEG has been studied to predict the depth of anesthesia using variety of signal processing methods up to date. Those standard models have used the full spectrum of EEG signals together with the systolic-diastolic pressure and pulse va...
Anesthetics, Inhalation Pulse Aged Male Dose-Response Relationship, Drug Heart Rate Middle Aged Humans Blood Pressure Wavelet Analysis Time Factors Neural Networks, Computer Electroencephalography Methyl Ethers Algorithms Signal Processing, Computer-Assisted Sevoflurane Female
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Modeling analgesic drug interactions using support vector regression: a new approach to isobolographic analysis.

Journal of pharmacological and toxicological methods Sep 28, 2014
BACKGROUND: Modeling drug interactions is important for illustrating combined drug actions and for predicting the pharmacological and/or toxicological effects that can be obtained using combined drug therapy.
Dose-Response Relationship, Drug Injections, Intraperitoneal Analgesics Drug Interactions Organoplatinum Compounds Peripheral Nervous System Diseases Male Mice Oxaliplatin Support Vector Machine Animals
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Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry Jun 15, 2025
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...
Humans Anti-Bacterial Agents Drug Discovery Deep Learning Multigene Family Antimicrobial Peptides Microbial Sensitivity Tests Dose-Response Relationship, Drug Structure-Activity Relationship Molecular Structure
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Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.

Journal of chemical information and modeling Jun 9, 2025
In early-stage drug design, machine learning models often rely on compressed representations of data, where raw experimental results are distilled into a single metric per molecule through curve fitting. This process discards valuable information abo...
Machine Learning Dose-Response Relationship, Drug Uncertainty
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Deep learning-assisted analysis of biomarker changes after increase of dosing from aflibercept 2 mg to 8 mg in therapy-resistant neovascular age-related macular degeneration.

BMJ open ophthalmology Jun 1, 2025
PURPOSE: Age-related macular degeneration (AMD) remains the leading cause of blindness in developed countries. There are many different intravitreal anti-vascular endothelial growth factor (VEGF) drugs available for the treatment of neovascular AMD (...
Dose-Response Relationship, Drug Visual Acuity Tomography, Optical Coherence Vascular Endothelial Growth Factor A Humans Deep Learning Aged Receptors, Vascular Endothelial Growth Factor Intravitreal Injections Biomarkers Angiogenesis Inhibitors Aged, 80 and over Fluorescein Angiography Retrospective Studies Treatment Outcome Male Recombinant Fusion Proteins Female Follow-Up Studies Middle Aged Wet Macular Degeneration
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Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols.

Journal of translational medicine May 15, 2025
BACKGROUND: Individualizing follicle-stimulating hormone (FSH) dosing during controlled ovarian stimulation (COS) is critical for optimizing outcomes in assisted reproduction but remains difficult due to patient heterogeneity. Most existing models ar...
Humans Precision Medicine Retrospective Studies Dose-Response Relationship, Drug Follicle Stimulating Hormone Female Gonadotropin-Releasing Hormone Adult Ovulation Induction Deep Learning
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